Senior Data Scientist / ML

About the Role

We are seeking a highly skilled and mission-driven Data Scientist to join our customer team and lead the development of our Patient Risk Scoring machine learning initiatives. In this role, you will build predictive models that identify high-risk patients for interventions, ultimately improving patient outcomes and optimizing care delivery. You will leverage Microsoft Fabric to manage end-to-end machine learning workflows, drawing insights from Electronic Health Record (EHR) data sources. If you are passionate about the intersection of advanced artificial intelligence and healthcare innovation, we want to hear you.

Responsibilities

• Model Development & Deployment: Design, train, evaluate, and deploy machine learning models to predict patient risk scores (e.g., medication refusal, non-compliance, decompensation etc.).
• EHR Data Engineering & Processing: Extract, clean, and transform healthcare data from EHR systems (e.g., Credible) to build robust feature sets for predictive modeling.
• End-to-End Analytics with Microsoft Fabric: Utilize Microsoft Fabric’s unified analytics platform (including Data Engineering, Data Science, and Real-Time Analytics workloads) to orchestrate data pipelines, manage Lakehouse architectures, and scale ML training/inference.
• Clinical Collaboration: Partner closely with clinical stakeholders, medical officers, and care teams to define risk cohorts, ensure the clinical validity of model features, and translate model outputs into actionable clinical workflows.
• MLOps & Monitoring: Establish continuous integration, deployment, and monitoring of ML models to track data drift, model degradation, and fairness/bias over time.
• Compliance & Privacy: Ensure all data handling and modeling practices strictly adhere to healthcare regulations (e.g., HIPAA, HITRUST) and maintain the highest standards of data security and patient privacy.

Qualifications

• Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Bioinformatics, Health Informatics, or a related quantitative field.
• Professional Experience: 4+ years of experience developing and deploying machine learning solutions in production environments, preferably within healthcare or clinical data ecosystems.
• Microsoft Fabric Experience: Hands-on experience building and deploying data and ML workflows within the Microsoft Fabric ecosystem (OneLake, Notebooks, Spark, Data Factory).
• Machine Learning Proficiency: Strong grasp of classical machine learning algorithms (e.g., XGBoost, Random Forests, Logistic Regression) and modern deep learning techniques, specifically for tabular and time-series data.
• Programming Skills: Advanced proficiency in Python and SQL. Experience with data manipulation and ML libraries (Pandas, PySpark, Scikit-Learn, PyTorch, or TensorFlow).
• Communication: Excellent ability to translate complex technical and statistical concepts to non-technical clinical and business stakeholders.

Nice to Have

• Cloud Certifications: Relevant Microsoft Azure or Fabric certifications.

Work Environment & Collaboration

• Full-time: 8 hours a day.

• Schedule: Mon – Fri 9-5 (US EST) overlap with team at least 4 hours.

• Team Structure: Work independently on assigned tasks with support from experienced team members.

• Communication: Primarily asynchronous via email and MS Teams.